Idiopathic aldosteronism biomarker and application thereof

Abstract

The invention belongs to the technical field of biological detection, and particularly relates to an idiopathic aldosteronism biomarker and application thereof. The idiopathic aldosteronism biomarker is a human body intestinal microorganism, and the human body intestinal microorganism comprises one or a combination of more of desulfurization vibrio, peptostreptococcaceae, ruminococcus and prevotella. A diagnostic model constructed on the basis of the biomarkers (two or more of devulcanivibrio, peptostreptococcus, ruminococcus or prevotella) provided by the invention can be used for auxiliary diagnosis of idiopathic aldosteronism, and is high in accuracy, excellent in sensitivity and good in specificity.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of biological detection, and particularly relates to a biomarker for idiopathic hyperaldosteronism and application thereof. BACKGROUND

[0002] Primary aldosteronism (PA, referred to as primary aldosteronism) as the most common type of secondary hypertension, accounts for about 5% to 10% of all hypertensive patients, and its core pathological feature is the autonomous secretion of aldosterone caused by adrenal cortical lesions. Compared with primary hypertensive patients matched in age, gender and blood pressure level, the incidence and mortality of cardiovascular adverse events (including myocardial infarction, chronic heart failure, arrhythmia, stroke, etc.) and chronic kidney disease of PA patients are significantly increased, which poses a serious threat to the life and health of patients. In the clinical classification of PA, idiopathic hyperaldosteronism (IHA, referred to as idiopathic hyperaldosteronism) is the main subtype, and its etiology and treatment scheme have clear specificity: unilateral aldosterone tumor patients are recommended for surgical treatment, and IHA patients usually need long-term drug intervention through oral salt corticosteroid receptor antagonists (such as spironolactone); therefore, accurate diagnosis of IHA is the key prerequisite for individualized treatment of PA patients and reduction of disease risk.

[0003] The current clinical diagnosis process of IHA is extremely cumbersome, and needs to complete multiple links such as drug elution, screening test, diagnosis test, adrenal computer tomography (CT) and adrenal vein blood sampling (AVS) in turn. This process is not only complex and time-consuming, but also contains invasive operation steps such as AVS, which has the operation risks of bleeding, infection, etc., and the examination cost is high, resulting in that some patients delay diagnosis and treatment due to fear of risk or economic burden. From the clinical needs, the development of IHA diagnosis technology with non-invasive operation, simple process, high safety and controllable cost has important clinical value and practical significance for improving the efficiency of disease diagnosis, improving the patient's medical experience, and reducing the risk of cardiovascular and cerebrovascular diseases and death.

[0004] As a biochemical indicator that can mark the changes of body tissue and cell function, biomarkers are widely used in disease diagnosis, staging and efficacy evaluation. Among them, feces is an ideal biomarker carrier due to its non-invasive collection, large sample size and easy access. Existing studies (such as Front Endocrinol. 2021; 12: 667951) have found that the intestinal flora of PA patients is different from that of non-hypertensive control population, specifically manifested as a decrease in alpha diversity, a decrease in the abundance of short-chain fatty acid-producing bacteria (Prevotella, Blautia, etc.), an increase in the abundance of inflammation-related bacteria (Megasphaera, Sutterella, etc.), and 30 kinds of bacteria (including Blautia, Lactobacillus, etc.) can be used as PA diagnostic markers, with an area under the ROC curve (AUC) of 0.8173. However, it should be noted that there are essential differences between IHA and PA in pathogenesis, and biomarkers reflecting the pathological characteristics of IHA should be clearly distinguished from the overall markers of PA. However, there is no report on intestinal flora biomarkers that can accurately reflect the specificity of IHA and use feces as a carrier in the prior art. SUMMARY

[0005] Based on this, the present application finds and verifies that the abundance of Desulfobacterales ( Desulfovibrionales ), Peptostreptococcaceae ( Peptostreptococcaceae ), Ruminococcus ( Ruminococcus ) and Prevotellaceae ( Prevotellaceae ) in the intestinal microorganisms of patients with idiopathic hyperaldosteronism is significantly different from that of healthy people, and the above four flora can be used as biomarkers for the diagnosis of idiopathic hyperaldosteronism, with excellent diagnostic accuracy.

[0006] In order to achieve the above purpose, the present application can adopt the following technical solutions: The present application provides a kind of idiopathic hyperaldosteronism biomarker, and the idiopathic hyperaldosteronism biomarker is human intestinal microorganism, and human intestinal microorganism includes two or more of Desulfobacterium ( Desulfovibrionales ), Peptostreptococcus ( Peptostreptococcaceae ), Ruminococcus ( Ruminococcus ) or Prevotella ( Prevotellaceae ).

[0007] Preferably, the above-mentioned idiopathic hyperaldosteronism biomarker is selected from any one of the following combinations: (a) Desulfobacterium, Peptostreptococcus, Ruminococcus and Prevotella combination; (b) Peptostreptococcus, Prevotella and Ruminococcus combination; (c) Desulfobacterium, Prevotella and Ruminococcus combination; (d) Desulfobacterium, Peptostreptococcus and Ruminococcus combination; (e) a combination of Desulfovibrio, Peptostreptococcus and Prevotella.

[0008] Preferably, the idiopathic hyperaldosteronism biomarker is a combination of Desulfovibrio and Peptostreptococcus.

[0009] In another aspect, the present application provides a use of a detection reagent of the idiopathic hyperaldosteronism biomarker in the present application in the preparation of a diagnostic reagent or kit for idiopathic hyperaldosteronism.

[0010] Preferably, in the use, the detection reagent of the idiopathic hyperaldosteronism biomarker comprises a detection reagent based on PCR amplification sequencing, a detection reagent based on fluorescent quantitative qPCR, a detection reagent based on metagenomic sequencing, or a detection reagent based on metabolomics analysis.

[0011] More preferably, in the use, The detection reagent based on PCR amplification sequencing comprises a DNA extraction reagent and / or a PCR amplification primer; The detection reagent based on fluorescent quantitative qPCR comprises one or more combinations of a DNA extraction reagent, a PCR amplification primer, a fluorescent reagent, or a fluorescent probe; The detection reagent based on metagenomic sequencing comprises a pre-sequencing treatment reagent and / or a shotgun library construction reagent; The detection reagent based on metabolomics analysis comprises one or more combinations of a T700 targeted metabolomics reagent, a Q500 full quantitative metabolomics reagent, an internal standard, and / or a liquid chromatography mobile phase.

[0012] In another aspect, the present application provides a diagnostic reagent or kit for idiopathic hyperaldosteronism, which comprises the detection reagent of the idiopathic hyperaldosteronism biomarker.

[0013] Preferably, in the diagnostic reagent or kit for idiopathic hyperaldosteronism, the detection reagent of the idiopathic hyperaldosteronism biomarker comprises a detection reagent based on PCR amplification sequencing, a detection reagent based on fluorescent quantitative qPCR, a detection reagent based on metagenomic sequencing, or a detection reagent based on metabolomics analysis.

[0014] More preferably, in the diagnostic reagent or kit for idiopathic hyperaldosteronism, The detection reagent based on PCR amplification sequencing comprises a DNA extraction reagent and / or a PCR amplification primer; The detection reagent based on fluorescent quantitative qPCR comprises one or more combinations of a DNA extraction reagent, a PCR amplification primer, a fluorescent reagent, or a fluorescent probe; The detection reagent based on metagenomic sequencing comprises a pre-sequencing treatment reagent and / or a shotgun library construction reagent; The detection reagents based on metabolomics analysis include one or more combinations of T700 targeted metabolome reagents, Q500 full quantitative metabolome reagents, internal standards, and / or liquid chromatography mobile phases.

[0015] The beneficial effects of the present application include: the diagnostic model constructed based on the biomarkers provided by the present application (one or more combinations of Desulfuromonadales, Peptostreptococcaceae, Pediococcus, or Prevotellaceae) can be used for the auxiliary diagnosis of idiopathic hyperaldosteronism, with high accuracy, excellent sensitivity, and good specificity; especially the three combinations of Desulfuromonadales, Peptostreptococcaceae, and Pediococcus as markers, through binary logistic regression and ROC analysis, the AUC value of the three combinations as markers can reach 0.926, the sensitivity can reach 100.0%, and the specificity can reach 77.8%. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 Shannon index of intestinal flora of IHA and HC subjects in Example 1; Figure 2 Differences in intestinal flora at the genus level between the two groups of IHA and HC subjects in Example 1; Figure 3 Statistical results of the relative abundance of four bacteria in Example 1; Figure 4 ROC curve of the training set based on the four biomarker combinations in Example 2; Figure 5 ROC curve of the validation set based on the four biomarker combinations in Example 2. DETAILED DESCRIPTION

[0017] The examples are provided to better illustrate the present application, but are not intended to limit the present application to only the examples. Therefore, non-essential improvements and modifications to the embodiments made by those skilled in the art based on the above disclosure are still within the scope of the present application.

[0018] The terms used herein are used only to describe specific embodiments and are not intended to limit the present disclosure. Unless otherwise apparent in context, the singular forms "an," "a," and "the" include plural forms. As used herein, it is understood that terms such as "include," "have," "comprise," and the like are intended to indicate the presence of the described features, numbers, operations, components, parts, elements, materials, or combinations thereof. The terms of the present application are disclosed in the specification, and are not intended to exclude the possibility that one or more other features, numbers, operations, components, parts, elements, materials, or combinations thereof can exist or can be added. As used herein, " / " can be interpreted as "and" or "or" depending on the situation.

[0019] In a first aspect, the embodiments of the present application provide a biomarker for idiopathic hyperaldosteronism, the biomarker for idiopathic hyperaldosteronism being human intestinal microorganisms, the human intestinal microorganisms including two or more of Desulfovibrio (Desulfovibrio sp.) Desulfovibrionales ), Peptostreptococcus (Peptostreptococcus sp.) Peptostreptococcaceae ), Ruminococcus (Ruminococcus sp.) Ruminococcus ) or Prevotella (Prevotella sp.) Prevotellaceae ).

[0020] It should be noted that the present application finds and verifies that the abundance of Desulfovibrio, Peptostreptococcus, Ruminococcus and Prevotella in the intestinal microorganisms of patients with idiopathic hyperaldosteronism is significantly different from that of healthy people, and two or more of the above four groups of bacteria can be used as biomarkers for the diagnosis of idiopathic hyperaldosteronism in different combinations.

[0021] In some specific examples, the biomarker for idiopathic hyperaldosteronism is selected from any of the following combinations: (a) Desulfovibrio, Peptostreptococcus, Ruminococcus and Prevotella combination; specifically, the AUC value of the Desulfovibrio, Peptostreptococcus, Ruminococcus and Prevotella combination as a marker can reach 0.926, the sensitivity can reach 100.0%, and the specificity can reach 77.8%; (b) Peptostreptococcus, Prevotella and Ruminococcus combination; specifically, the AUC value of the Peptostreptococcus, Prevotella and Ruminococcus combination as a marker can reach 0.840; (c) Desulfovibrio, Prevotella and Ruminococcus combination; specifically, the AUC value of the Desulfovibrio, Prevotella and Ruminococcus combination as a marker can reach 0.901; (d) Desulfovibrio, Peptostreptococcus and Ruminococcus combination; specifically, the AUC value of the Desulfovibrio, Peptostreptococcus and Ruminococcus combination can reach 0.914; (e) Desulfovibrio, Peptostreptococcus and Prevotella combination; specifically, the AUC value of the Desulfovibrio, Peptostreptococcus and Prevotella combination can reach 0.790.

[0022] In a second aspect, the embodiments of the present application provide a use of a detection reagent for the biomarker for idiopathic hyperaldosteronism in the present application in the preparation of a diagnostic reagent or kit for idiopathic hyperaldosteronism.

[0023] It should be noted that the marker in the present application has high accuracy when diagnosing idiopathic hyperaldosteronism, and the detection reagent of the marker can be prepared into a diagnostic reagent or kit for idiopathic hyperaldosteronism; the form of the diagnostic reagent or kit is known in the art.

[0024] In some specific examples, in the above applications, the detection reagent for the idiopathic hyperaldosteronism biomarker comprises a PCR amplification sequencing-based detection reagent, a fluorescence quantitative qPCR-based detection reagent, a metagenomic sequencing-based detection reagent, or a metabolomics analysis-based detection reagent.

[0025] It should be noted that the detection reagent for the idiopathic hyperaldosteronism biomarker in the present application is a detection reagent for a flora known in the art, such as the reagents listed above.

[0026] In some specific examples, in the above applications, The PCR amplification sequencing-based detection reagent comprises a DNA extraction reagent and / or a PCR amplification primer; The fluorescence quantitative qPCR-based detection reagent comprises one or more combinations of a DNA extraction reagent, a PCR amplification primer, a fluorescence reagent, or a fluorescence probe; The metagenomic sequencing-based detection reagent comprises a pre-sequencing treatment reagent and / or a shotgun library construction reagent; The metabolomics analysis-based detection reagent comprises one or more combinations of a T700 targeted metabolomics reagent, a Q500 full quantitative metabolomics reagent, an internal standard, and / or a liquid chromatography mobile phase.

[0027] It should be noted that the PCR amplification sequencing-based detection reagent, the fluorescence quantitative qPCR-based detection reagent, the metagenomic sequencing-based detection reagent, or the metabolomics analysis-based detection reagent described above are known in the art, including but not limited to the reagents listed above.

[0028] In a third aspect, an embodiment of the present application provides an idiopathic hyperaldosteronism diagnostic reagent or kit, which comprises the detection reagent for the idiopathic hyperaldosteronism biomarker described above.

[0029] It should be noted that the detection reagent for the idiopathic hyperaldosteronism biomarker described above can be prepared into an idiopathic hyperaldosteronism diagnostic reagent or kit, and the preparation method is known in the art.

[0030] In some specific examples, in the above applications, the detection reagent for the idiopathic hyperaldosteronism biomarker comprises a PCR amplification sequencing-based detection reagent, a fluorescence quantitative qPCR-based detection reagent, a metagenomic sequencing-based detection reagent, or a metabolomics analysis-based detection reagent.

[0031] In some specific examples, in the above applications, The detection reagent based on PCR amplification sequencing includes DNA extraction reagent and / or PCR amplification primer; The detection reagent based on fluorescence quantitative qPCR includes one or more combinations of DNA extraction reagent, PCR amplification primer, fluorescence reagent or fluorescence probe; The detection reagent based on metagenomic sequencing includes pre-sequence treatment reagent and / or shotgun library construction reagent; The detection reagent based on metabolomics analysis includes one or more combinations of T700 targeted metabolomics reagent, Q500 full quantitative metabolomics reagent, internal standard and / or liquid chromatography mobile phase.

[0032] In order to better understand the present application, the content of the present application is further illustrated below in combination with specific examples, but the content of the present application is not limited only to the following examples.

[0033] Example 1 The embodiment of the present application provides a biomarker screening process for idiopathic hyperaldosteronism (IHA, referred to as idiopathic hyperaldosteronism).

[0034] (1) Mendelian randomization analysis (1) Data source In the following analysis, the data of intestinal flora comes from the Microbiome Genome (MiBioGen) alliance website (mibiogen.gcc.rug.nl / menu / main / home), which includes 24 cohorts and a total of 18340 research subjects from different races; DNA is extracted from human fecal samples, and 16S rRNA gene sequencing is used to annotate human intestinal flora to the "genus" category.

[0035] In the following analysis, the data of idiopathic hyperaldosteronism comes from the Chongqing primary aldosteronism study (CONPASS) cohort; DNA is extracted from human blood, and whole genome sequencing is used to obtain single nucleotide polymorphism (SNP) related to primary aldosteronism (especially idiopathic hyperaldosteronism).

[0036] (2) Two-sample Mendelian randomization analysis between intestinal flora and IHA-related SNPs 1) Select highly correlated (P<1x10 -5SNPs, finally 14587 different SNPs were included; Inverse Variance Weighted (IVW) was used for analysis, and the results are shown in Table 1.

[0037] Table 1 Causal association between intestinal flora and IHA predicted by IVW method Note: The specific meanings of the abbreviations and superscripts in the above table are as follows: SNP (Single Nucleotide Polymorphism): single nucleotide polymorphism; OR (Odds Ratio): odds ratio; CI (Confidence Interval): confidence interval.

[0038] From Table 1 above, it can be seen that Eubacterium (genus level, Eubacterium nodatum group ), Peptostreptococcaceae (family level, Peptostreptococcaceae ), Ruminococcus UCG003 (genus level, Ruminococcaceae UCG003 ), Ruminococcus 1 (genus level, Ruminococcus 1 ), Desulfovibrionales (order level, Desulfovibrionales ) have a negative causal relationship with IHA and are protective factors for IHA; and Ruminococcus UCG014 (genus level, Ruminococcaceae UCG014 ), Prevotellaceae (family level, Prevotellaceae ) have a positive causal relationship with IHA and are pathogenic factors for IHA.

[0039] 2) MR-Egger regression was used to test the pleiotropy of the instrumental variables, and the results are shown in Table 2 below, which shows that the selected instrumental variables do not have pleiotropy (all P>0.05).

[0040] Table 2 Pleiotropy analysis of the causal association between intestinal flora and IHA (II) Metagenomic sequencing analysis (1) Study subjects A total of 60 subjects were recruited for this analysis, aged 32-71 years old, of which 30 were healthy people (Healthy Control, HC) and 30 were idiopathic hyperaldosteronism (IHA); In addition, all patients who met the inclusion criteria of this study signed the informed consent form and agreed to participate in this clinical trial (approved by the Clinical Research Ethics Committee of the First Affiliated Hospital of Chongqing Medical University, ethical approval number: 2024-002-01); There was no statistically significant difference in age, gender and BMI between the two groups. For the detailed information of the subjects, please see Table 3.

[0041] Table 3 Information of subjects Note: The specific meanings of the abbreviations and superscripts in the table above are as follows: BMI (Body Mass Index): Body Mass Index; ARR (Aldosterone Renin Ratio): Aldosterone-renin ratio; a Chi-square test b Student T test (t-test).

[0042] (2) Sample collection and pretreatment Participants provided fresh stool samples, which were immediately placed in sterile tubes and placed on ice. The stool samples were aliquoted in the laboratory and stored at -80°C until subsequent sequencing. After all samples were collected, total DNA was extracted using the FastPure Stool DNA Isolation Kit. DNA purity was assessed using NanoDrop2000, and DNA concentration was determined using Synergy HTX. The DNA was fragmented into approximately 350bp fragments using a Covaris M220 ultrasonic disruptor. Libraries were constructed using the NEXTFLEXRapid DNA-Seq Kit.

[0043] (3) Bridge PCR and sequencing parameter settings Metagenomic sequencing of the constructed library was performed using the Illumina NovaSeq™ X Plus sequencing platform. The specific workflow is as follows: 1) One end of the library molecule is complementary to the primer bases. After one round of amplification, the template information is fixed on the chip. 2) The other end of the molecule fixed on the chip is randomly complementary to another nearby primer and is also fixed, forming a "bridge"; 3) PCR amplification produces DNA clusters; 4) DNA amplicon linearizes into a single strand; 5) Add modified DNA polymerase and dNTPs with 4 fluorescent labels, and synthesize only one base per cycle; 6) Use a laser to scan the surface of the reaction plate and read the types of nucleotides that were polymerized in the first round of reaction for each template sequence; 7) Chemically cleave the "fluorescent group" and "terminator group" to restore the 3' end stickiness and continue polymerizing the second nucleotide; 8) Analyze the fluorescence signal results collected in each round to obtain the sequence of the template DNA fragment.

[0044] (4) Data Analysis Whole genome sequencing was performed on Illumina platform to obtain 150 bp long forward and reverse paired reads. The raw data was pretreated using Trimmomatic v0.39 to obtain clean data; the specific processing steps are as follows: 1) remove low-quality reads containing more than 35 bp 'N' bases (default quality threshold < 15); 2) delete reads with a certain length (default length is 10 bp) of overlapping sequence with primers; 3) use Bowtie2.4.1 software (http: / / bowtie-bio.sourceforge.net / bowtie2 / index.shtml) to align the reads in the clean data with the human genome reference database GRCh38, and filter out the reads from the host; the parameters are as follows: --end-to-end, --more-sensitive, -I 200, -X 500; 4) use Megahit software (v1.2.9, https: / / github.com / voutcn / megahit) to assemble the high-quality sequences pretreated for each sample from scratch, and output Contigs of at least 500 bp or more for subsequent analysis; 5) Contigs (≥500 bp) assembled for each sample are predicted by ORF using MetaGeneMark software (v2.10, http: / / topaz.gatech.edu / GeneMark / ), and the ORF with a length less than 100 nt is filtered from the prediction results using default parameters, and CD-HIT software (v4.8.1, http: / / www.bioinformatics.org / cd-hit) is used for redundancy removal to obtain a non-redundant gene set; 6) use featureCounts software (v2.0.1, http: / / subread.sourceforge.net) to align the clean data of each sample to the non-redundant gene set to calculate the number of reads aligned to each gene; 7) use FPKM standardization to obtain gene relative abundance information; 8) Non-redundant gene sequences were aligned against the NR database of NCBI (Version 2023.04, https: / / www.ncbi.nlm.nih.gov / ) using MMseqs2 software (v13.45111, https: / / github.com / soedinglab / mmseqs2); 9) The final species annotation information of each gene was determined using the approximate 2bLCA algorithm.

[0045] Through the above metagenomic test analysis, a total of 5 phylum-level species, 111 phylum-level species, 150 phylum-level species, 282 phylum-level species, 555 phylum-level species, 1651 phylum-level species, and 7521 phylum-level species were identified; among them, the Shannon index of the HC group was higher than that of the IHA group (P < 0.05). Figure 1 The results showed that the intestinal flora alpha diversity level of the IHA patients was significantly lower than that of the HC group.

[0046] In addition, the Linear Discriminant Analysis (LDA) effect size (LEfSe) method was used to identify the differentially abundant bacteria between the two groups. At the genus level, the relative abundance of 26 bacteria was significantly different between the two groups (P < 0.05). Figure 2 At the species level, the relative abundance of 117 bacteria was significantly different between the two groups.

[0047] (Three) Screening of biomarkers capable of identifying idiopathic hyperaldosteronism In order to study the intestinal flora biomarkers related to the diagnosis of idiopathic hyperaldosteronism, further evaluation of the different levels of flora found by Mendelian randomization analysis and metagenomic sequencing analysis was carried out, as follows: Through metagenomic sequencing research, 4 of the 7 bacteria found by Mendelian randomization in the previous two samples were detected (see Table 4 below), which were Desulfovibrionales (up-regulated in IHA) Desulfovibrionales ), Ruminococcus (down-regulated in IHA) Ruminococcus ), as well as Veillonellaceae (no difference between the two groups) Peptostreptococcaceae ) and Prevotellaceae (no difference between the two groups) Prevotellaceae ), all of which can be used as biomarkers for idiopathic hyperaldosteronism (although Veillonellaceae and Prevotellaceae showed no difference in metagenomic analysis, but showed difference in Mendelian randomization analysis, so they can be used as markers) Figure 3 .

[0048] Table 4 Basic information of 4 bacteria In addition, the cases of Desulfovibrio, Peptostreptococcus, Streptococcus ruminantium and Prevotella are shown in Table 5 below.

[0049] Table 5 Functional description of biomarkers Example 2 (I) Four biomarker combinations The embodiments of the present application provide a process for constructing a diagnostic model based on the above-mentioned biomarkers of idiopathic hyperaldosteronism and verification, which are as follows: (1) After completing the above-mentioned marker screening, 60 subjects (30 healthy people (HC) and 30 people with idiopathic hyperaldosteronism (IHA) are recruited, and the requirements and procedures for the subjects are the same as those for the subjects and procedures for recruiting in the above-mentioned metagenomic sequencing analysis); (2) The 60 subjects are randomly divided into a training set and a validation set in a ratio of 7:3, wherein the training set includes 21 IHA and 21 HC, and the validation set includes 9 IHA and 9 HC; (3) Based on the four bacteria, a diagnostic model is established using binary logistic regression (the diagnostic variable (independent variable) is the relative abundance of the four biomarkers, and the dependent variable is the IHA and the healthy person HC, i.e. the binary variable of whether the IHA is present), and a receiver operating characteristic (ROC) analysis is performed to quantify the diagnostic performance of the biomarker combination containing the four bacteria; wherein the binary logistic regression and the ROC analysis are both performed using the conventional statistical software SPSS21.0 (IBM Corp., Armonk, NY, USA) of the prior art; the ROC curve is used to evaluate the goodness of the classification and detection results, and is a very important and common statistical analysis method, which is a coordinate graph composed of the false positive rate (1-specificity) as the horizontal axis and the true positive rate (sensitivity) as the vertical axis. Different results obtained by the test sample at different judgment standards (threshold) are drawn into a curve; the area under the curve AUC is used to represent the accuracy, and the higher the AUC value, the higher the accuracy.

[0050] The ROC curves in the training set and the validation set are shown in Figure 4 and Figure 5 The area under the ROC curve (AUC) value, 95% confidence interval (CI), sensitivity and specificity are shown in Table 6 below.

[0051] Table 6 AUC value, 95% confidence interval (CI), sensitivity and specificity in the training set and the validation set It can be seen from Table 6 that the accuracy, sensitivity and specificity of the diagnostic model constructed by using binary logistic regression based on the biomarkers provided by the present application are relatively high.

[0052] (II) combination of Streptococcus, Prevotella and Ruminococcus Streptococcus, Prevotella and Ruminococcus were selected as biomarkers for diagnosing idiopathic hyperaldosteronism, and binary logistic regression and ROC analysis were performed according to the above method. The results showed that the AUC value of the diagnostic model was 0.840. The above results show that after excluding Desulfovibrio, the AUC value of the diagnostic model decreases.

[0053] (III) combination of Desulfovibrio, Prevotella and Ruminococcus Desulfovibrio, Prevotella and Ruminococcus were selected as biomarkers for diagnosing idiopathic hyperaldosteronism, and binary logistic regression and ROC analysis were performed according to the above method. The results showed that the AUC value of the diagnostic model was 0.901. The above results show that after excluding Streptococcus, the AUC value of the diagnostic model decreases.

[0054] (IV) combination of Desulfovibrio, Streptococcus and Ruminococcus Desulfovibrio, Streptococcus and Ruminococcus were selected as biomarkers for diagnosing idiopathic hyperaldosteronism, and binary logistic regression and ROC analysis were performed according to the above method. The results showed that the AUC value of the diagnostic model was 0.914. After excluding Prevotella, the AUC value of the diagnostic model decreases.

[0055] (V) combination of Desulfovibrio, Streptococcus and Prevotella Desulfovibrio, Streptococcus and Prevotella were selected as biomarkers for diagnosing idiopathic hyperaldosteronism, and binary logistic regression and ROC analysis were performed according to the above method. The AUC value of the diagnostic model was 0.790. After excluding Ruminococcus, the AUC value of the diagnostic model decreases.

[0056] Finally, it should be pointed out that the above examples are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, and all should be covered in the scope of the claims of the present application.

Claims

1. A biomarker for idiopathic hyperaldosteronism, characterized in that, The biomarker of idiopathic hyperaldosteronism is human intestinal microorganism, and the human intestinal microorganism includes three or more than three of Desulfovibrio, Peptostreptococcus, Ruminococcus or Prevotella. Desulfovibrionales Peptostreptococcaceae Ruminococcus Prevotellaceae The biomarker of idiopathic hyperaldosteronism is selected from any one of the following combinations:​​​ (a) a combination of Desulfovibrionales, Peptostreptococcaceae, Ruminococcus, and Prevotellaceae; (b) a combination of Peptostreptococcaceae, Ruminococcus, and Prevotellaceae; (c) a combination of Desulfovibrionales, Ruminococcus, and Prevotellaceae; (d) a combination of Desulfovibrionales, Peptostreptococcaceae, and Ruminococcus; (e) a combination of Desulfovibrionales, Peptostreptococcaceae, and Prevotellaceae.

2. Use of a detection reagent for a biomarker of idiopathic hyperaldosteronism according to claim 1 in the preparation of a diagnostic reagent or kit for idiopathic hyperaldosteronism.

3. Use according to claim 2, characterized in that, The detection reagent for a biomarker of idiopathic hyperaldosteronism includes a PCR amplification sequencing-based detection reagent, a fluorescence quantitative qPCR-based detection reagent, a metagenomic sequencing-based detection reagent, or a metabolomics analysis-based detection reagent.

4. The use according to claim 3, wherein, The PCR amplification sequencing-based detection reagent includes a DNA extraction reagent and / or PCR amplification primers; The fluorescence quantitative qPCR-based detection reagent includes one or more combinations of a DNA extraction reagent, PCR amplification primers, a fluorescence reagent, or a fluorescence probe; The metagenomic sequencing-based detection reagent includes a pre-sequencing treatment reagent and / or a shotgun library construction reagent; The metabolomics analysis-based detection reagent includes one or more combinations of a T700 targeted metabolome reagent, a Q500 full quantitative metabolome reagent, an internal standard, and / or a liquid chromatography mobile phase.

5. A diagnostic reagent or kit for idiopathic hyperaldosteronism, characterized by, The detection reagent for a biomarker of idiopathic hyperaldosteronism according to claim 1.

6. The diagnostic reagent or kit for diagnosing idiopathic hyperaldosteronism according to claim 5, wherein The detection reagent for a biomarker of idiopathic hyperaldosteronism includes a PCR amplification sequencing-based detection reagent, a fluorescence quantitative qPCR-based detection reagent, a metagenomic sequencing-based detection reagent, or a metabolomics analysis-based detection reagent.

7. The diagnostic reagent or kit for idiopathic hyperaldosteronism according to claim 6, wherein, The PCR amplification sequencing-based detection reagent includes a DNA extraction reagent and / or PCR amplification primers; The fluorescence quantitative qPCR-based detection reagent includes one or more combinations of a DNA extraction reagent, PCR amplification primers, a fluorescence reagent, or a fluorescence probe; The metagenomic sequencing-based detection reagent includes a pre-sequencing treatment reagent and / or a shotgun library construction reagent; The metabolomics analysis-based detection reagent includes one or more combinations of a T700 targeted metabolome reagent, a Q500 full quantitative metabolome reagent, an internal standard, and / or a liquid chromatography mobile phase.

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